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1a008c1cb8
docs
best_practices
deploying_clearml/enterprise_deploy
img/gif
dataset.gifdataset_dark.gifinfra_control_plane.gifinfra_control_plane_dark.gifintegrations_yolov5.gifintegrations_yolov5_dark.gif
integrations
@ -9,7 +9,8 @@ See [Hyper-Datasets](../hyperdatasets/overview.md) for ClearML's advanced querya
|
||||
|
||||
The following are some recommendations for using ClearML Data.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Versioning Datasets
|
||||
|
||||
|
||||
@ -0,0 +1,78 @@
|
||||
---
|
||||
title: Changing CleaML Artifacts Links
|
||||
---
|
||||
|
||||
This guide describes how to update artifact references in the ClearML Enterprise server.
|
||||
|
||||
By default, artifacts are stored on the file server; however, an external storage such as AWS S3, Minio, Google Cloud
|
||||
Storage, etc. may be used to store artifacts. References to these artifacts may exist in ClearML databases: MongoDB and ElasticSearch.
|
||||
This procedure should be used if external storage is being migrated to a different location or URL.
|
||||
|
||||
:::important
|
||||
This procedure does not deal with the actual migration of the data--only with changing the references in ClearML that
|
||||
point to the data.
|
||||
:::
|
||||
|
||||
## Preparation
|
||||
|
||||
### Version Confirmation
|
||||
|
||||
To change the links, use the `fix_fileserver_urls.py` script, located inside the `allegro-apiserver`
|
||||
Docker container. This script will be executed from within the `apiserver` container. Make sure the `apiserver` version
|
||||
is 3.20 or higher.
|
||||
|
||||
### Backup
|
||||
|
||||
It is highly recommended to back up the ClearML MongoDB and ElasticSearch databases before running the script, as the
|
||||
script changes the values in the databases, and can't be undone.
|
||||
|
||||
## Fixing MongoDB links
|
||||
|
||||
1. Access the `apiserver` Docker container:
|
||||
* In `docker-compose:`
|
||||
|
||||
```commandline
|
||||
sudo docker exec -it allegro-apiserver /bin/bash
|
||||
```
|
||||
|
||||
* In Kubernetes:
|
||||
|
||||
```commandline
|
||||
kubectl exec -it -n clearml <clearml-apiserver-pod-name> -- bash
|
||||
```
|
||||
|
||||
1. Navigate to the script location in the `upgrade` folder:
|
||||
|
||||
```commandline
|
||||
cd /opt/seematics/apiserver/server/upgrade
|
||||
```
|
||||
|
||||
1. Run the following command:
|
||||
|
||||
:::important
|
||||
Before running the script, verify that this is indeed the correct version (`apiserver` v3.20 or higher,
|
||||
or that the script provided by ClearML was copied into the container).
|
||||
::::
|
||||
|
||||
```commandline
|
||||
python3 fix_fileserver_urls.py \
|
||||
--mongo-host mongodb://mongo:27017 \
|
||||
--elastic-host elasticsearch:9200 \
|
||||
--host-source "<old fileserver host and/or port, as in artifact links>" \
|
||||
--host-target "<new fileserver host and/or port>" --datasets
|
||||
```
|
||||
|
||||
:::note Notes
|
||||
* If MongoDB or ElasticSearch services are accessed from the `apiserver` container using custom addresses, then
|
||||
`--mongo-host` and `--elastic-host` arguments should be updated accordingly.
|
||||
* If ElasticSearch is set up to require authentication then the following arguments should be used to pass the user
|
||||
and password: `--elastic-user <es_user> --elastic-password <es_pass>`
|
||||
:::
|
||||
|
||||
The script fixes the links in MongoDB, and outputs `cURL` commands for updating the links in ElasticSearch.
|
||||
|
||||
## Fixing the ElasticSearch Links
|
||||
|
||||
Copy the `cURL` commands printed by the script run in the previous stage, and run them one after the other. Make sure to
|
||||
inspect that a "success" result was returned from each command. Depending on the amount of the data in the ElasticSearch,
|
||||
running these commands may take some time.
|
||||
240
docs/deploying_clearml/enterprise_deploy/import_projects.md
Normal file
240
docs/deploying_clearml/enterprise_deploy/import_projects.md
Normal file
@ -0,0 +1,240 @@
|
||||
---
|
||||
title: Exporting and Importing ClearML Projects
|
||||
---
|
||||
|
||||
When migrating from a ClearML Open Server to a ClearML Enterprise Server, you may need to transfer projects. This is done
|
||||
using the `data_tool.py` script. This utility is available in the `apiserver` Docker image, and can be used for
|
||||
exporting and importing ClearML project data for both open source and Enterprise versions.
|
||||
|
||||
This guide covers the following:
|
||||
* Exporting data from Open Source and Enterprise servers
|
||||
* Importing data into an Enterprise server
|
||||
* Handling the artifacts stored on the file server.
|
||||
|
||||
:::note
|
||||
Export instructions differ for ClearML open and Enterprise servers. Make sure you follow the guidelines that match your
|
||||
server type.
|
||||
:::
|
||||
|
||||
## Exporting Data
|
||||
|
||||
The export process is done by running the ***data_tool*** script that generates a zip file containing project and task
|
||||
data. This file should then be copied to the server on which the import will run.
|
||||
|
||||
Note that artifacts stored in the ClearML ***file server*** should be copied manually if required (see [Handling Artifacts](#handling-artifacts)).
|
||||
|
||||
### Exporting Data from ClearML Open Servers
|
||||
|
||||
#### Preparation
|
||||
|
||||
* Make sure the `apiserver` is at least Open Source server version 1.12.0.
|
||||
* Note that any `pending` or `running` tasks will not be exported. If you wish to export them, make sure to stop/dequeue
|
||||
them before exporting.
|
||||
|
||||
#### Running the Data Tool
|
||||
|
||||
Execute the data tool within the `apiserver` container.
|
||||
|
||||
Open a bash session inside the `apiserver` container of the server:
|
||||
* In docker-compose:
|
||||
|
||||
```commandline
|
||||
sudo docker exec -it clearml-apiserver /bin/bash
|
||||
```
|
||||
|
||||
* In Kubernetes:
|
||||
|
||||
```commandline
|
||||
kubectl exec -it -n <clearml-namespace> <clearml-apiserver-pod-name> -- bash
|
||||
```
|
||||
|
||||
#### Export Commands
|
||||
**To export specific projects:**
|
||||
|
||||
```commandline
|
||||
python3 -m apiserver.data_tool export --projects <project_id1> <project_id2>
|
||||
--statuses created stopped published failed completed --output <output-file-name>.zip
|
||||
```
|
||||
|
||||
As a result, you should get a `<output-file-name>.zip` file that contains all the data from the specified projects and
|
||||
their children.
|
||||
|
||||
**To export all the projects:**
|
||||
|
||||
```commandline
|
||||
python3 -m apiserver.data_tool export \
|
||||
--all \
|
||||
--statuses created stopped published failed completed \
|
||||
--output <output-file-name>.zip
|
||||
```
|
||||
|
||||
#### Optional Parameters
|
||||
|
||||
* `--experiments <list of experiment IDs>` - If not specified then all experiments from the specified projects are exported
|
||||
* `--statuses <list of task statuses>` - Export tasks of specific statuses. If the parameter
|
||||
is omitted, only `published` tasks are exported
|
||||
* `--no-events` - Do not export task events, i.e. logs and metrics (scalar, plots, debug samples).
|
||||
|
||||
Make sure to copy the generated zip file containing the exported data.
|
||||
|
||||
### Exporting Data from ClearML Enterprise Servers
|
||||
|
||||
#### Preparation
|
||||
|
||||
* Make sure the `apiserver` is at least Enterprise Server version 3.18.0.
|
||||
* Note that any `pending` or `running` tasks will not be exported. If you wish to export them, make sure to stop/dequeue
|
||||
before exporting.
|
||||
|
||||
#### Running the Data Tool
|
||||
|
||||
Execute the data tool from within the `apiserver` docker container.
|
||||
|
||||
Open a bash session inside the `apiserver` container of the server:
|
||||
* In `docker-compose`:
|
||||
|
||||
```commandline
|
||||
sudo docker exec -it allegro-apiserver /bin/bash
|
||||
```
|
||||
|
||||
* In Kubernetes:
|
||||
|
||||
```commandline
|
||||
kubectl exec -it -n <clearml-namespace> <clearml-apiserver-pod-name> -- bash
|
||||
```
|
||||
|
||||
#### Export Commands
|
||||
|
||||
**To export specific projects:**
|
||||
|
||||
```commandline
|
||||
PYTHONPATH=/opt/seematics/apiserver/trains-server-repo python3 data_tool.py \
|
||||
export \
|
||||
--projects <project_id1> <project_id2> \
|
||||
--statuses created stopped published failed completed \
|
||||
--output <output-file-name>.zip
|
||||
```
|
||||
|
||||
As a result, you should get `<output-file-name>.zip` file that contains all the data from the specified projects and
|
||||
their children.
|
||||
|
||||
**To export all the projects:**
|
||||
|
||||
```commandline
|
||||
PYTHONPATH=/opt/seematics/apiserver/trains-server-repo python3 data_tool.py \
|
||||
export \
|
||||
--all \
|
||||
--statuses created stopped published failed completed \
|
||||
--output <output-file-name>.zip
|
||||
```
|
||||
|
||||
#### Optional Parameters
|
||||
|
||||
* `--experiments <list of experiment IDs>` - If not specified then all experiments from the specified projects are exported
|
||||
* `--statuses <list of task statuses>` - Can be used to allow exporting tasks of specific statuses. If the parameter is
|
||||
omitted, only `published` tasks are exported.
|
||||
* `--no-events` - Do not export task events, i.e. logs, and metrics (scalar, plots, debug samples).
|
||||
|
||||
Make sure to copy the generated zip file containing the exported data.
|
||||
|
||||
## Importing Data
|
||||
|
||||
This section explains how to import the exported data into a ClearML Enterprise server.
|
||||
|
||||
### Preparation
|
||||
|
||||
* It is highly recommended to back up the ClearML databases before importing data, as import injects data into the
|
||||
databases, and can't be undone.
|
||||
* Make sure you are working with `apiserver` version 3.22.3 or higher.
|
||||
* Make the zip file accessible from within the `apiserver` container by copying the exported data to the
|
||||
`apiserver` container or to a folder on the host, which the `apiserver` is mounted to.
|
||||
|
||||
### Usage
|
||||
|
||||
The data tool should be executed from within the `apiserver` docker container.
|
||||
|
||||
1. Open a bash session inside the `apiserver` container of the server:
|
||||
* In `docker-compose`:
|
||||
|
||||
```commandline
|
||||
sudo docker exec -it allegro-apiserver /bin/bash
|
||||
```
|
||||
|
||||
* In Kubernetes:
|
||||
|
||||
```commandline
|
||||
kubectl exec -it -n <clearml-namespace> <clearml-apiserver-pod-name> -- bash
|
||||
```
|
||||
|
||||
1. Run the data tool script in *import* mode:
|
||||
|
||||
```commandline
|
||||
PYTHONPATH=/opt/seematics/apiserver/trains-server-repo python3 data_tool.py \
|
||||
import \
|
||||
<path to zip file> \
|
||||
--company <company_id> \
|
||||
--user <user_id>
|
||||
```
|
||||
|
||||
* `company_id`- The default company ID used in the target deployment. Inside the `apiserver` container you can
|
||||
usually get it from the environment variable `CLEARML__APISERVER__DEFAULT_COMPANY`.
|
||||
If you do not specify the `--company` parameter then all the data will be imported as `Examples` (read-only)
|
||||
* `user_id` - The ID of the user in the target deployment who will become the owner of the imported data
|
||||
|
||||
## Handling Artifacts
|
||||
|
||||
***Artifacts*** refers to any content which the ClearML server holds references to. This can include:
|
||||
* Dataset or Hyper-Dataset frame URLs
|
||||
* ClearML artifact URLs
|
||||
* Model snapshots
|
||||
* Debug samples
|
||||
|
||||
Artifacts may be stored in any external storage (e.g., AWS S3, minio, Google Cloud Storage) or in the ClearML file server.
|
||||
* If the artifacts are **not** stored in the ClearML file server, they do not need to be moved during the export/import process,
|
||||
as the URLs registered in ClearML entities pointing to these artifacts will not change.
|
||||
* If the artifacts are stored in the ClearML file server, then the file server content must also be moved, and the URLs
|
||||
in the ClearML databases must point to the new location. See instructions [below](#exporting-file-server-data-for-clearml-open-server).
|
||||
|
||||
### Exporting File Server Data for ClearML Open Server
|
||||
|
||||
Data in the file server is organized by project. For each project, all data references by entities in that project is
|
||||
stored in a folder bearing the name of the project. This folder can be located in:
|
||||
|
||||
```
|
||||
/opt/clearml/data/fileserver/<project name>
|
||||
```
|
||||
|
||||
The entire projects' folders content should be copied to the target server (see [Importing Fileserver Data](#importing-file-server-data)).
|
||||
|
||||
### Exporting File Server Data for ClearML Enterprise Server
|
||||
|
||||
Data in the file server is organized by tenant and project. For each project, all data references by entities in that
|
||||
project is stored in a folder bearing the name of the project. This folder can be located in:
|
||||
|
||||
```
|
||||
/opt/allegro/data/fileserver/<company_id>/<project name>
|
||||
```
|
||||
|
||||
The entire projects' folders content should be copied to the target server (see [Importing Fileserver Data](#importing-file-server-data)).
|
||||
|
||||
## Importing File Server Data
|
||||
|
||||
### Copying the Data
|
||||
|
||||
Place the exported projects' folder(s) content into the target file server's storage in the following folder:
|
||||
|
||||
```
|
||||
/opt/allegro/data/fileserver/<company_id>/<project name>
|
||||
```
|
||||
|
||||
### Fixing Registered URLs
|
||||
|
||||
Since URLs pointing to the file server contain the file server's address, these need to be changed to the address of the
|
||||
new file server.
|
||||
|
||||
Note that this is not required if the new file server is replacing the old file server and can be accessed using the same
|
||||
exact address.
|
||||
|
||||
Once the projects' data has been copied to the target server, and the projects themselves were imported, see
|
||||
[Changing CleaML Artifacts Links](change_artifact_links.md) for information on how to fix the URLs.
|
||||
|
||||
|
||||
@ -0,0 +1,98 @@
|
||||
---
|
||||
title: Multi-Tenant Login Mode
|
||||
---
|
||||
|
||||
In a multi-tenant setup, each external tenant can be represented by an SSO client defined in the customer Identity provider
|
||||
(Keycloak). Each ClearML tenant can be associated with a particular external tenant. Currently, only one
|
||||
ClearML tenant can be associated with a particular external tenant
|
||||
|
||||
## Setup IdP/SSO Client in Identity Provider
|
||||
|
||||
1. Add the following URL to "Valid redirect URIs": `<clearml_webapp_address>/callback_<client_id>`
|
||||
2. Add the following URLs to "Valid post logout redirect URIs":
|
||||
|
||||
```
|
||||
<clearml_webapp_address>/login
|
||||
<clearml_webapp_address>/login/<external tenant ID>
|
||||
```
|
||||
3. Make sure the external tenant ID and groups are returned as claims for a each user
|
||||
|
||||
## Configure ClearML to use Multi-Tenant Mode
|
||||
|
||||
Set the following environment variables in the ClearML enterprise helm chart under the `apiserver` section:
|
||||
* To turn on the multi-tenant login mode:
|
||||
|
||||
```
|
||||
- name: CLEARML__services__login__sso__tenant_login
|
||||
value: "true"
|
||||
```
|
||||
* To hide any global IdP/SSO configuration that's not associated with a specific ClearML tenant:
|
||||
|
||||
```
|
||||
- name: CLEARML__services__login__sso__allow_settings_providers
|
||||
value: "false"
|
||||
```
|
||||
|
||||
Enable `onlyPasswordLogin` by setting the following environment variable in the helm chart under the `webserver` section:
|
||||
|
||||
```
|
||||
- name: WEBSERVER__onlyPasswordLogin`
|
||||
value: “true”`
|
||||
```
|
||||
|
||||
## Setup IdP for a ClearML Tenant
|
||||
|
||||
To set an IdP client for a ClearML tenant, you’ll need to set the ClearML tenant settings and define an identity provider:
|
||||
|
||||
1. Call the following API to set the ClearML tenant settings:
|
||||
|
||||
```
|
||||
curl $APISERVER_URL/system.update_company_sso_config -H "Content-Type: application/json" -u $APISERVER_KEY:$APISERVER_SECRET -d'{
|
||||
"company": "<company_id>",
|
||||
"sso": {
|
||||
"tenant": "<external tenant ID>",
|
||||
"group_mapping": {
|
||||
"IDP group name1": "Clearml group name1",
|
||||
"IDP group name2": "Clearml group name2"
|
||||
},
|
||||
"admin_groups": ["IDP admin group name1", "IDP admin group name2"]
|
||||
}}'
|
||||
```
|
||||
2. Call the following API to define the ClearML tenant identity provider:
|
||||
|
||||
```
|
||||
curl $APISERVER_URL/sso.save_provider_configuration -H "Content-Type: application/json" -u $APISERVER_KEY:$APISERVER_SECRET -d'{
|
||||
"provider": "keycloak",
|
||||
"company": "<company_id>",
|
||||
"configuration": {
|
||||
"id": "<some unique id here, you can use company_id>",
|
||||
"display_name": "<The text that you want to see on the login button>",
|
||||
"client_id": "<client_id from IDP>",
|
||||
"client_secret": "<client secret from IDP>",
|
||||
"authorization_endpoint": "<authorization_endpoint from IDP OpenID configuration>",
|
||||
"token_endpoint": "<token_endpoint from IDP OpenID configuration>",
|
||||
"revocation_endpoint": "<revocation_endpoint from IDP OpenID configuration>",
|
||||
"end_session_endpoint": "<end_session_endpoint from IDP OpenID configuration>",
|
||||
"logout_from_provider": true,
|
||||
"claim_tenant": "tenant_key",
|
||||
"claim_name": "name",
|
||||
"group_enabled": true,
|
||||
"claim_groups": "ad_groups_trusted",
|
||||
"group_prohibit_user_login_if_not_in_group": true
|
||||
}}'
|
||||
```
|
||||
The above configuration assumes the following:
|
||||
* On logout from ClearML, the user is also logged out from the Identity Provider
|
||||
* External tenant ID for the user is returned under the `tenant_key` claim
|
||||
* User display name is returned under the `name` claim
|
||||
* User groups list is returned under the `ad_groups_trusted` claim
|
||||
* Group integration is turned on and a user will be allowed to log in if any of the groups s/he belongs to in the
|
||||
IdP exists under the corresponding ClearML tenant (this is after group name translation is done according to the ClearML tenant settings)
|
||||
|
||||
## Webapp Login
|
||||
|
||||
When running in multi-tenant login mode, a user belonging to some external tenant should use the following link to log in:
|
||||
|
||||
```
|
||||
<clearml_webapp_address>/login/<external tenant ID>
|
||||
```
|
||||
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@ -95,7 +95,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -93,7 +93,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -92,7 +92,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -105,7 +105,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -94,7 +94,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -90,7 +90,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -114,7 +114,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -120,7 +120,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -96,7 +96,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -113,7 +113,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -107,7 +107,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -78,7 +78,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -120,7 +120,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -169,7 +169,8 @@ and shuts down instances as needed, according to a resource budget that you set.
|
||||
|
||||
### Cloning, Editing, and Enqueuing
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
||||
with the new configuration on a remote machine:
|
||||
|
||||
@ -166,4 +166,5 @@ with the new configuration on a remote machine:
|
||||
|
||||
The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent.md).
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
2
package-lock.json
generated
2
package-lock.json
generated
@ -15,7 +15,7 @@
|
||||
"@docusaurus/plugin-google-analytics": "^3.6.1",
|
||||
"@docusaurus/plugin-google-gtag": "^3.6.1",
|
||||
"@docusaurus/preset-classic": "^3.6.1",
|
||||
"@easyops-cn/docusaurus-search-local": "^0.48.0",
|
||||
"@easyops-cn/docusaurus-search-local": "^0.48.5",
|
||||
"@mdx-js/react": "^3.0.0",
|
||||
"clsx": "^1.1.1",
|
||||
"joi": "^17.4.0",
|
||||
|
||||
@ -23,7 +23,7 @@
|
||||
"@docusaurus/plugin-google-analytics": "^3.6.1",
|
||||
"@docusaurus/plugin-google-gtag": "^3.6.1",
|
||||
"@docusaurus/preset-classic": "^3.6.1",
|
||||
"@easyops-cn/docusaurus-search-local": "^0.48.0",
|
||||
"@easyops-cn/docusaurus-search-local": "^0.48.5",
|
||||
"@mdx-js/react": "^3.0.0",
|
||||
"clsx": "^1.1.1",
|
||||
"medium-zoom": "^1.0.6",
|
||||
|
||||
@ -652,6 +652,8 @@ module.exports = {
|
||||
]
|
||||
},
|
||||
'deploying_clearml/enterprise_deploy/delete_tenant',
|
||||
'deploying_clearml/enterprise_deploy/import_projects',
|
||||
'deploying_clearml/enterprise_deploy/change_artifact_links',
|
||||
{
|
||||
'Enterprise Applications': [
|
||||
'deploying_clearml/enterprise_deploy/app_install_ubuntu_on_prem',
|
||||
@ -671,6 +673,7 @@ module.exports = {
|
||||
label: 'Identity Provider Integration',
|
||||
link: {type: 'doc', id: 'user_management/identity_providers'},
|
||||
items: [
|
||||
'deploying_clearml/enterprise_deploy/sso_multi_tenant_login',
|
||||
'deploying_clearml/enterprise_deploy/sso_saml_k8s',
|
||||
'deploying_clearml/enterprise_deploy/sso_keycloak',
|
||||
'deploying_clearml/enterprise_deploy/sso_active_directory'
|
||||
|
||||
Loading…
Reference in New Issue
Block a user